tables and data wrangling

Author

…

Published

April 13, 2026

Assignment: Who is Samantha Power?

Task 1. Samantha Power asks: ‘Most students don’t realize that France and the UK have not used the veto since 1989. Not the case for the US, Russia, and China. Why is that?’ Who is she such that she might find this question especially interesting?

Task 2. Name two or three area of international relations (is it also security council politics?) that interests you, broadly speaking? Foreign aid? International conflict? Security Council Voting? Foreign direct Investment?

Task 3. If we try to frame the question as a research question we might say there is an ‘outcome to be explained’ (or variation in the world to explain): France and UK are not using veto where as China, US, and Russia do. The variation is binary, does-doesn’t, type variation. In your areas of interest, is there some variation in the world that we can observe? Amount of foreign aid? Aid flows? Votes on the council? Investment flows?

Task 3. There are several explanations for the variation that Power’s notices:

relative difference in material capabilities/power of the permanent members.
change in the relative salience of issues voted upon in the Security Council.
strong relationships between France and UK with non permanent members
something else?

… What are some of the possible explanations for the variation that you are interested in?

Examples:

Amount of foreign aid <- Need
Amount of foreign aid <- placating behavior?

wrapping up … ‘layers’ in ggplot2

Create a plot with assignments:

geom_line()
geom_histogram()
geom_rug()
geom_density_2d()
geom_dotplot()

library(tidyverse)
library(gapminder)

#
gapminder <- gapminder |> 
  mutate(life_exp_cats = 
           case_when(lifeExp >= 70 ~ "Long Life Expectancy",
                     lifeExp < 70  & lifeExp >= 50 ~ "Medium Life Expectancy",
                     lifeExp < 50 ~ "Short Life Expectancy"),
         income_cat = 
           case_when(gdpPercap >= 30000 ~ "High Income",
                     gdpPercap < 3000  & lifeExp >= 10000 ~ "Medium Inncome",
                     gdpPercap < 1000 ~ "Low Income"))


### data subsets
gap_2002 <- gapminder |> 
  filter(year == 2002)

gap_americas <- gapminder |> 
  filter(continent == "Americas")

# univariate continuous

  

# bivariate, 2 continuous


# multi series

Again Take one of the assigned ‘geoms’ from the cheat sheet or grammar guide we haven’t already used, and create a plot and be prepared to describe it:

geom_boxplot
geom_violin
geom_jitter
geom_count
geom_bin2d

library(ggplot2)
library(gapminder)

### data subset
gap_2002 <- gapminder |> 
  filter(year == 2002)


# bivariate, continuous X discrete



# bivariate, discrete X discrete



# bivariate, continuous X continuous

ggplot2 cheat sheet -> language school classroom posters?

New agenda item: tables, and data manipulation (‘wrangling’)…

data cleaning, wrangling, tidydata

Wrangle. “From Middle English wranglen, from Low German wrangeln (“to wrangle”), frequentative form of Low German wrangen (“to struggle, make an uproar”) and German rangeln (“to wrestle”).”

“He who has a why to live can bear almost any how.” – Friedrich Nietzsche

https://www.youtube.com/watch?v=GELhdezYmP0

library(tidyverse)

“Tidy Data”

Analyst frustration:

Within columns, information is ‘like-in-kind’

–

like in kind?

All numerics, all Strings, all factors (categories), all logical (TRUE/FALSE), all ordered factors (categories with an order).
Also, no rows with summaries - ‘raw’ table

Observational unit?

library(tidyverse)
uruk_3200bc <- tribble(~item, ~amount,
        "hop", 42,
        "barley", 84)

uruk_3200bc

# A tibble: 2 × 2
  item   amount
  <chr>   <dbl>
1 hop        42
2 barley     84

uruk_3200bc |> 
  ggplot() + 
  aes(x = item,
      y = amount,
      fill = item) + 
  geom_col() + 
  labs(title = "'We can only imagine what they were making...' 🙃")

is the gapminder data tidy?

library(gapminder)
gapminder

# A tibble: 1,704 × 8
   country     continent  year lifeExp    pop gdpPercap life_exp_cats income_cat
   <fct>       <fct>     <int>   <dbl>  <int>     <dbl> <chr>         <chr>     
 1 Afghanistan Asia       1952    28.8 8.43e6      779. Short Life E… Low Income
 2 Afghanistan Asia       1957    30.3 9.24e6      821. Short Life E… Low Income
 3 Afghanistan Asia       1962    32.0 1.03e7      853. Short Life E… Low Income
 4 Afghanistan Asia       1967    34.0 1.15e7      836. Short Life E… Low Income
 5 Afghanistan Asia       1972    36.1 1.31e7      740. Short Life E… Low Income
 6 Afghanistan Asia       1977    38.4 1.49e7      786. Short Life E… Low Income
 7 Afghanistan Asia       1982    39.9 1.29e7      978. Short Life E… Low Income
 8 Afghanistan Asia       1987    40.8 1.39e7      852. Short Life E… Low Income
 9 Afghanistan Asia       1992    41.7 1.63e7      649. Short Life E… Low Income
10 Afghanistan Asia       1997    41.8 2.22e7      635. Short Life E… Low Income
# ℹ 1,694 more rows

Wrangling exploration…

In class…

Wrangling ‘moves’

Full data set, for reference

library(gapminder)
gapminder

# A tibble: 1,704 × 8
   country     continent  year lifeExp    pop gdpPercap life_exp_cats income_cat
   <fct>       <fct>     <int>   <dbl>  <int>     <dbl> <chr>         <chr>     
 1 Afghanistan Asia       1952    28.8 8.43e6      779. Short Life E… Low Income
 2 Afghanistan Asia       1957    30.3 9.24e6      821. Short Life E… Low Income
 3 Afghanistan Asia       1962    32.0 1.03e7      853. Short Life E… Low Income
 4 Afghanistan Asia       1967    34.0 1.15e7      836. Short Life E… Low Income
 5 Afghanistan Asia       1972    36.1 1.31e7      740. Short Life E… Low Income
 6 Afghanistan Asia       1977    38.4 1.49e7      786. Short Life E… Low Income
 7 Afghanistan Asia       1982    39.9 1.29e7      978. Short Life E… Low Income
 8 Afghanistan Asia       1987    40.8 1.39e7      852. Short Life E… Low Income
 9 Afghanistan Asia       1992    41.7 1.63e7      649. Short Life E… Low Income
10 Afghanistan Asia       1997    41.8 2.22e7      635. Short Life E… Low Income
# ℹ 1,694 more rows

Wrangling syntax

function	action
filter()	keep rows (if true)
select()	keep variables (or drop them -var)
mutate()	create a new variable
distinct()	keep unique
case_when()	is used for “recoding” variable, often used with mutate()
rename()	renaming variables
arrange()	order rows based on a variable
slice()	*keep or drop rows based on row number

Wrangling ‘moves’ answers….